Job Description
Recruitment Consultant
Bliss Verna
Contact Details
[email protected] +44 (0) 3333XXXXXX
Posted
5 hours ago
We’re partnering with a venture-backed AI startup building next-generation visual conversational systems – enabling users to interact with AI through real-time video experiences that feel genuinely human. Their work sits at the intersection of multimodal ML, high-performance infrastructure, and real-time systems. They’re now hiring a Machine Learning Research Engineer to help bring cutting-edge models from research into highly optimized production environments.
This is a hands-on role focused on GPU performance, model acceleration, and scaling multimodal systems in real-world deployments.
Key Responsibilities:
• Collaborate with research teams to productionize experimental models and transition them into reliable, scalable systems.
• Own model performance optimization, profiling inference for latency and throughput improvements using techniques such as quantization, pruning, and distillation.
• Debug and optimize GPU workloads, including CUDA-level performance issues.
• Apply acceleration frameworks (e.g., TensorRT, ONNX, vLLM) to improve multimodal model efficiency across video, speech, and LLM systems.
• Design and implement high-throughput data pipelines for large-scale video and multimedia datasets (petabyte-scale).
• Develop evaluation frameworks to measure model quality and support continuous iteration.
• Work closely with infrastructure teams to build scalable media processing and training workflows.
Key Qualifications:
• 2+ years of full-time experience in ML engineering, ideally in production ML environments.
• Proven experience operationalizing research models into production systems with a focus on inference optimization (latency and throughput improvements).
• Strong proficiency in PyTorch for training and deploying large-scale models.
• Experience running models across large datasets for feature extraction or batch inference workloads.
• Hands-on experience working with GPUs and debugging CUDA-related performance issues.
• Experience with video, audio, or multimodal models preferred.
• Background in a VC-backed startup or high-performing established company is advantageous.
• Willingness to work onsite 5 days per week in Seattle.
• Strong hands-on engineering focus (not a management-leaning or director-level profile).
• Candidates whose ML experience is limited to fine-tuning models without deeper systems or performance work are unlikely to be a fit.
Industry
AI & Machine Learning
Contract Type
Permanent
Location
United States
City
Seattle
Work Model
On-Site
Bliss Verna
Contact Details
[email protected] +44 (0) 3333XXXXXX
Posted
5 hours ago
We’re partnering with a venture-backed AI startup building next-generation visual conversational systems – enabling users to interact with AI through real-time video experiences that feel genuinely human. Their work sits at the intersection of multimodal ML, high-performance infrastructure, and real-time systems. They’re now hiring a Machine Learning Research Engineer to help bring cutting-edge models from research into highly optimized production environments.
This is a hands-on role focused on GPU performance, model acceleration, and scaling multimodal systems in real-world deployments.
Key Responsibilities:
• Collaborate with research teams to productionize experimental models and transition them into reliable, scalable systems.
• Own model performance optimization, profiling inference for latency and throughput improvements using techniques such as quantization, pruning, and distillation.
• Debug and optimize GPU workloads, including CUDA-level performance issues.
• Apply acceleration frameworks (e.g., TensorRT, ONNX, vLLM) to improve multimodal model efficiency across video, speech, and LLM systems.
• Design and implement high-throughput data pipelines for large-scale video and multimedia datasets (petabyte-scale).
• Develop evaluation frameworks to measure model quality and support continuous iteration.
• Work closely with infrastructure teams to build scalable media processing and training workflows.
Key Qualifications:
• 2+ years of full-time experience in ML engineering, ideally in production ML environments.
• Proven experience operationalizing research models into production systems with a focus on inference optimization (latency and throughput improvements).
• Strong proficiency in PyTorch for training and deploying large-scale models.
• Experience running models across large datasets for feature extraction or batch inference workloads.
• Hands-on experience working with GPUs and debugging CUDA-related performance issues.
• Experience with video, audio, or multimodal models preferred.
• Background in a VC-backed startup or high-performing established company is advantageous.
• Willingness to work onsite 5 days per week in Seattle.
• Strong hands-on engineering focus (not a management-leaning or director-level profile).
• Candidates whose ML experience is limited to fine-tuning models without deeper systems or performance work are unlikely to be a fit.
Industry
AI & Machine Learning
Contract Type
Permanent
Location
United States
City
Seattle
Work Model
On-Site
💡 Quick Summary
Seeking a career-building opportunity? The ML Research Engineer position is now open for candidates interested in the IT Engineer & Developer Jobs sector. This role in Seattle offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in IT Engineer & Developer Jobs is a plus.
